Machine Learning Identifies Robust Matrisome Markers and Regulatory Mechanisms in Cancer

Pan-cancer and multiomics: advanced strategies for diagnosis, prognosis, and therapy in the complex genetic and molecular universe of cancer

The pan-cancer and multi-omics approach is motivated by the genetic and molecular complexity inherent in the varied types of cancer. This method presents itself as a crucial resource for advancing early diagnosis, defining prognoses and identifying treatments that share common bases between different forms of tumors. The aim of this article is to explore pan-cancer analysis in conjunction with multi-omics strategies, evaluating laboratory, computational, clinical procedures and their consequences, as well as examining the tumor microenvironment, epigenetics and future directions of these technologies in patient management. To this end, a literature review was conducted using PUBMED, resulting in the selection of 260 articles, of which 81 were carefully chosen to support this analysis. The pan-cancer methodology is applied to the study of this microenvironment with the aim of investigating its common characteristics through multiomics data. The development of new therapies depends on understanding the oncogenic pathways associated with different cancers. Thus, the integration of multi-omics and pan-cancer analyzes offers an innovative perspective in the search for new control points, metabolic pathways and markers, in addition to facilitating the identification of patterns common to multiple cancer types, allowing the development of targeted treatments. In this way, the convergence of multiomics and clinical approaches promotes a broad view of cancer biology, leading to more effective and personalized therapies.

Machine learning-based approach for automated classification of cell and extracellular matrix using nanomechanical properties

Fibrosis characterized by excess accumulation of extracellular matrix (ECM) due to complex cell-ECM interactions plays a pivotal role in pathogenesis. Herein, we employ the pancreatic ductal adenocarcinoma (PDAC) model to investigate dynamic alterations in nanomechanical attributes arising from the cell-ECM interactions to study the fibrosis paradigm. Several segregated studies performed on cellular and ECM components fail to recapitulate their complex collaboration. We utilized collagen and fibronectin, the two most abundant PDAC ECM components, and studied their nanomechanical attributes. We demonstrate alteration in morphology and nanomechanical attributes of collagen with varying thicknesses of collagen gel. Furthermore, by mixing collagen and fibronectin in various stoichiometry, their nanomechanical attributes were observed to vary. To demonstrate the dynamicity and complexity of cell-ECM, we utilized Panc-1 and AsPC-1 cells with or without collagen. We observed that Panc-1 and AsPC-1 cells interact differently with collagen and vice versa, evident from their alteration in nanomechanical properties. Further, using nanomechanics data, we demonstrate that ML-based techniques were able to classify between ECM as well as cell, and cell subtypes in the presence/absence of collagen with higher accuracy. This work demonstrates a promising avenue to explore other ECM components facilitating deeper insights into tumor microenvironment and fibrosis paradigm.

Recreating the extracellular matrix: novel 3D cell culture platforms in cancer research

Cancer initiation and progression heavily rely on microenvironmental cues derived from various components of the niche including the extracellular matrix (ECM). ECM is a complex macromolecular network that governs cell functionality. Although the two-dimensional (2D) cell culture systems provide useful information at the molecular level and preclinical testing, they could not accurately represent the in vivo matrix microenvironmental architecture. Hence, it is no surprise that researchers in the last decade have focussed their efforts on establishing novel advanced in vitro culture models that mimic tumour and tissue-specific niches and interactions. These numerous three-dimensional (3D) culture systems that are now widely available, as well as those still under development, grant researchers with new, improved tools to study cancer progression and to explore innovative therapeutic options. Herein, we report on the emerging methods and cutting-edge technologies in 3D cell culture platforms and discuss their potential use in unveiling tumour microenvironmental cues, drug screening and personalized treatment.

Tumor collagens predict genetic features and patient outcomes

The extracellular matrix (ECM) is a critical determinant of tumor fate that reflects the output from myriad cell types in the tumor. Collagens constitute the principal components of the tumor ECM. The changing collagen composition in tumors along with their impact on patient outcomes and possible biomarkers remains largely unknown. The RNA expression of the 43 collagen genes from solid tumors in The Cancer Genome Atlas (TCGA) was clustered to classify tumors. PanCancer analysis revealed how collagens by themselves can identify the tissue of origin. Clustering by collagens in each cancer type demonstrated strong associations with survival, specific immunoenvironments, somatic gene mutations, copy number variations, and aneuploidy. We developed a machine learning classifier that predicts aneuploidy, and chromosome arm copy number alteration (CNA) status based on collagen expression alone with high accuracy in many cancer types with somatic mutations, suggesting a strong relationship between the collagen ECM context and specific molecular alterations. These findings have broad implications in defining the relationship between cancer-related genetic defects and the tumor microenvironment to improve prognosis and therapeutic targeting for patient care, opening new avenues of investigation to define tumor ecosystems.

A pan-cancer analysis of matrisome proteins reveals CTHRC1 and a related network as major ECM regulators across cancers

The extracellular matrix in the tumour microenvironment can regulate cancer cell growth and progression. A pan-cancer analysis of TCGA data from 30 cancer types, identified the top 5% of matrisome genes with amplifications or deletions in their copy number, that affect their expression and cancer survival. A similar analysis of matrisome genes in individual cancers identified CTHRC1 to be significantly altered. CTHRC1, a regulator of collagen synthesis, was identified as the most prominently upregulated matrisome gene of interest across cancers. Differential gene expression analysis identified 19 genes whose expression is increased with CTHRC1. STRING analysis of these genes classified them as ‘extracellular’, involved most prominently in ECM organization and cell adhesion. KEGG analysis showed their involvement in ECM-receptor and growth factor signalling. Cytohubba analysis of these genes revealed 13 hub genes, of which MMP13, POSTN, SFRP4, ADAMTS16 and FNDC1 were significantly altered in their expression with CTHRC1 and seen to affect survival across cancers. This could in part be mediated by their overlapping roles in regulating ECM (collagen or fibronectin) expression and organisation. In breast cancer tumour samples CTHRC1 protein levels are significantly upregulated with POSTN and MMP13, further supporting the need to evaluate their crosstalk in cancers.

A functional outside-in signaling network of proteoglycans and matrix molecules regulating autophagy

Proteoglycans and selected extracellular matrix constituents are emerging as intrinsic and critical regulators of evolutionarily conversed, intracellular catabolic pathways. Often, these secreted molecules evoke sustained autophagy in a variety of cell types, tissues, and model systems. The unique properties of proteoglycans have ushered in a paradigmatic shift to broaden our understanding of matrix-mediated signaling cascades. The dynamic cellular pathway controlling autophagy is now linked to an equally dynamic and fluid signaling network embedded in a complex meshwork of matrix molecules. A rapidly emerging field of research encompasses multiple matrix-derived candidates, representing a menagerie of soluble matrix constituents including decorin, biglycan, endorepellin, endostatin, collagen VI and plasminogen kringle 5. These matrix constituents are pro-autophagic and simultaneously anti-angiogenic. In contrast, perlecan, laminin α2 chain, and lumican have anti-autophagic functions. Mechanistically, each matrix constituent linked to intracellular catabolic events engages a specific cell surface receptor that often converges on a common core of the autophagic machinery including AMPK, Peg3 and Beclin 1. We consider this matrix-evoked autophagy as non-canonical given that it occurs in an allosteric manner and is independent of nutrient availability or prevailing bioenergetics control. We propose that matrix-regulated autophagy is an important outside-in signaling mechanism for proper tissue homeostasis that could be therapeutically leveraged to combat a variety of diseases.

The Burden of Post-Translational Modification (PTM)-Disrupting Mutations in the Tumor Matrisome

BACKGROUND

To evaluate the occurrence of mutations affecting post-translational modification (PTM) sites in matrisome genes across different tumor types, in light of their genomic and functional contexts and in comparison with the rest of the genome.

METHODS

This study spans 9075 tumor samples and 32 tumor types from The Cancer Genome Atlas (TCGA) Pan-Cancer cohort and identifies 151,088 non-silent mutations in the coding regions of the matrisome, of which 1811 affecting known sites of hydroxylation, phosphorylation, N- and O-glycosylation, acetylation, ubiquitylation, sumoylation and methylation PTM.

RESULTS

PTM-disruptive mutations (PTMmut) in the matrisome are less frequent than in the rest of the genome, seem independent of cell-of-origin patterns but show dependence on the nature of the matrisome protein affected and the background PTM types it generally harbors. Also, matrisome PTMmut are often found among structural and functional protein regions and in proteins involved in homo- and heterotypic interactions, suggesting potential disruption of matrisome functions.

CONCLUSIONS

Though quantitatively minoritarian in the spectrum of matrisome mutations, PTMmut show distinctive features and damaging potential which might concur to deregulated structural, functional, and signaling networks in the tumor microenvironment.

A guide to the composition and functions of the extracellular matrix

Extracellular matrix (ECM) is a dynamic 3-dimensional network of macromolecules that provides structural support for the cells and tissues. Accumulated knowledge clearly demonstrated over the last decade that ECM plays key regulatory roles since it orchestrates cell signaling, functions, properties and morphology. Extracellularly secreted as well as cell-bound factors are among the major members of the ECM family. Proteins/glycoproteins, such as collagens, elastin, laminins and tenascins, proteoglycans and glycosaminoglycans, hyaluronan, and their cell receptors such as CD44 and integrins, responsible for cell adhesion, comprise a well-organized functional network with significant roles in health and disease. On the other hand, enzymes such as matrix metalloproteinases and specific glycosidases including heparanase and hyaluronidases contribute to matrix remodeling and affect human health. Several cell processes and functions, among them cell proliferation and survival, migration, differentiation, autophagy, angiogenesis, and immunity regulation are affected by certain matrix components. Structural alterations have been also well associated with disease progression. This guide on the composition and functions of the ECM gives a broad overview of the matrisome, the major ECM macromolecules, and their interaction networks within the ECM and with the cell surface, summarizes their main structural features and their roles in tissue organization and cell functions, and emphasizes the importance of specific ECM constituents in disease development and progression as well as the advances in molecular targeting of ECM to design new therapeutic strategies.